Walrus Memory

Walrus Memory is a portable memory layer designed for AI agents. It lets agents store and retrieve context across different apps, sessions, and runtimes. Instead of starting from scratch every time, agents can remember user preferences, past decisions, and workflow state. The memory is persistent, verifiable, and fully under the developer's control. You install it via npm or pip, configure a private key and account, and start calling remember() and recall() in your code.

What makes Walrus Memory stand out is its portability and coordination features. Memory doesn't die when a session ends — an agent can pick up where it left off in a different runtime weeks later. Programmable permissions let you decide how each memory is shared and accessed. For multi-agent workflows, agents share a single source of truth and stay coordinated on the same state. The system also provides an audit trail, so you can verify what an agent acted on. It works with major LLMs out of the box and has native plugins for OpenClaw and NemoClaw, plus MCP support.

This tool is built for developers creating AI agents that need reliable, long-term memory. It's especially useful for multi-agent workflows, customer support bots that remember past conversations, and personal assistants that learn user habits over time. If you're building production AI systems where context loss is a problem, Walrus Memory gives you a straightforward way to make memory persistent and portable.